207 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 © A. E. Sudakova, D.M.S. Dahel, 2023 doi 10.15826/recon.2023.9.2.013 UDC 378 JEL I22, I28, H52 Funding the Higher Education System: International Experience and Russian Practice A. E. Sudakovaa) , D.M.S. Dahelb)  a)Ural Federal University, Ekaterinburg, Russia;  ae.sudakova@gmail.com b)Al-Furat Al-Awsat Technical University;  mustafaaladli@gmail.com ABSTRACT Relevance. Education is a significant factor in economic growth. However, the dis- cussion about the principles for distributing higher education funding is still open, and the cases of individual countries are not sufficiently covered in research literature. Research objective. The study aims to determine the principles of financing based on the case studies of Russian universities. Foreign financing mechanisms are analyzed and compared with Russian practice that has similar foundations. Financing mechanisms are classified according to their distribution principle. Data and methods. The statistical base for the study is the data of a large-scale higher education monitoring project of 2019-2021. The study was conducted in more than 650 Russian universities. In order to determine the principles of financ- ing, a correlation analysis is carried out to identify the correlation between the in- dicators. Universities are grouped by regions with different socio-economic char- acteristics, subgroups of universities within the regional division were identified. Results. The distribution of funding among Russian universities is based on the principles of quasi-competition and equalization. Universities located in regions with low indicators of socio-economic development are mainly financed to achieve equalization of educational activities, and, as the socio-economic situation in the re- gion improves, funding is channeled into equalization of research activities. Anoth- er more obvious conclusion is that research activities of universities that participate in state programs are funded based on competition, while other universities have lower correlation between indicators, which leads us to the assumption that other universities’ research activities are funded based on the principles of equalization. Conclusions. The novelty of the study is the results that enrich the understanding of the principles for funding distribution in the Russian higher education system. Contrary to most studies of the concentration of resources around a limited num- ber of institutions, the study concludes that resources and funding are distributed based on equalization, supporting the less competitive units of the system, and directing funding to regions with less stable socio-economic characteristics. KEYWORDS higher education, financing higher education; competition; equalization; development of universities, funding universities ACKNOWLEDGEMENTS The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged FOR CITATION Sudakova, A. E., & Dahel, D. M. S. (2023). Funding the Higher Education System: International Experience and Russian Practice. R-Economy, 9(2), 207–225. doi: 10.15826/recon.2023.9.2.013 Финансирование системы высшего образования: международный опыт и российская практика А. Е. Судаковаa) , М. С. Д. Дахелb)  a)Уральский федеральный университет, Екатеринбург, Россия;  ae.sudakova@gmail.com b)Аль-Фурат Аль-Авсат технический университет, Наджаф, Ирак;  mustafaaladli@gmail.com АННОТАЦИЯ Актуальность. Образование является значимым фактором экономическо- го роста. При этом открытой остается дискуссия в части принципов рас- пределения финансирования высшего образования, и в то же время менее изученным вопросом с точки зрения освещения кейсов отдельных стран. Цель исследования. Исследование направлено на определение принципов финансирования на примере кейсов российских вузов. Проанализирова- ны зарубежные механизмы финансирования и представлено их сравнение с российской практикой, которая имеет схожие основы. Механизмы фи- нансирования классифицированы по принципу их распределения. КЛЮЧЕВЫЕ СЛОВА высшее образование, финансирование высшего образования; конкуренция; выравнивание; развитие университетов, финансирование университетов Original Paper http://r-economy.com https://doi.org/10.15826/recon.2023.9.1.003 mailto:ae.sudakova@gmail.com mailto:mustafaaladli@gmail.com mailto:ae.sudakova@gmail.com mailto:mustafaaladli@gmail.com 208 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Данные и методы. Статистической базой исследования служат данные мо- ниторинга высшего образования за 2019-2021 гг. Исследование проведено более чем по 650 российским вузам. С целью определения принципов фи- нансирования проводится корреляционный анализ на предмет выявления взаимосвязи между показателями. Произведена группировка вузов по реги- онам с различными социально-экономическими характеристиками, выделе- ны подгруппы вузов внутри регионального деления. Результаты. Распределение финансирования среди вузов РФ основывается на принципах квазиконкуренции и выравнивания. Вузы, расположенные в регионах с низкими показателями социально-экономического развития, финансируются преимущественно по принципу выравнивания в большей части относительно образовательной деятельности, по мере улучшения социально-экономического состояния региона, финансирование на усло- виях выравнивания направлено на научную деятельность вузов. Другой вывод, более очевидный, научная деятельность вузов-участников государ- ственных программ финансируется на условиях конкуренции, в то время как остальные вузы имеют меньшую зависимость между показателями, в результате чего, можем предположить, что финансирование научной де- ятельности остальных вузах основывается на принципах выравнивания. Выводы. Новизной исследования являются результаты, расширяющие представление о принципах распределения финансирования в системе российского высшего образования. В противовес большинству исследова- ний о концентрации ресурсов в кругу ограниченного количества вузов, в исследовании делается вывод, что распределение также осуществляется на условиях выравнивания, поддерживая менее конкурентоспособные еди- ницы системы, и направляя финансирование в регионы с менее устойчи- выми социально-экономическими характеристиками. БЛАГОДАРНОСТИ Исследование выполнено при финансовой поддержке Мини- стерства науки и высшего обра- зования Российской Федерации в рамках Программы развития Уральского федерального уни- верситета имени первого Пре- зидента России Б.Н. Ельцина в соответствии с программой стратегического академическо- го лидерства «Приоритет-2030» ДЛЯ ЦИТИРОВАНИЯ Sudakova, A. E., & Dahel, D. M. S. (2023). Funding the Higher Education System: International Experience and Russian Practice. R-Economy, 9(2), 207–225. doi: 10.15826/recon.2023.9.2.013 高等教育融资：国际经验与俄罗斯实践 苏达科娃a) 、达赫尔b)  a)乌拉尔联邦大学，叶卡捷琳堡，俄罗斯，邮箱：  ae.sudakova@gmail.com b) Al-Furat Al-Awsat技术大学，纳杰夫，伊拉克，邮箱：  mustafaaladli@gmail.com 摘要 现实性：教育是经济增长的一个重要因素。然而，关于高等教育资金分配原则的 辩论仍未结束，同时学术界在个别国家的相关案例研究也较少。 研究目标：本研究旨在以俄罗斯大学为例确定融资原则。文章对国外的融资机制 进行分析，并将其与具有类似基础的俄罗斯案例进行比较。融资机制将根据其分 配原则进行分类。 数据与方法：该研究的统计基础是2019-2021年的高等教育监测数据。研究对象 是650多所俄罗斯高等教育机构。为了确定融资原则，文章进行了相关分析，以 确定各指标之间的关系。高等教育机构按区域不同社会经济特征进行分组，并确 定区域中的高等教育机构子群。 研究结果：俄罗斯大学之间的经费分配遵循准竞争和均等的原则。位于社会经济 发展指标较低地区的高等院校在教育活动方面主要按照均衡化原则获得经费，随 着该地区社会经济状况的改善，均衡化融资被导向高等院校的科学活动。另一个 更明显的结论是，参与专项国家计划的学校的研究活动资金是通过竞争获得的， 而其他大学对该项目的依赖性较小，因此我们可以假设其他大学的研究活动资金 是基于均衡化原则。 结论：该研究的新颖之处在于扩大了对俄罗斯高等教育系统资金分配原则的理 解。与大多数关于资源集中在有限数量的高等教育机构圈子的研究相反，该研究 的结论是，分配是在均衡的基础上进行的，国家项目支持系统中竞争力较弱的单 位，并将资金引向社会经济特征不那么持久的地区。 关键词 高等教育、高等教育经费；竞赛； 均衡性; 大学发展、大学融资 致谢 衷心感谢俄罗斯联邦科学和高等教 育部（2030 年优先计划——乌拉 尔联邦大学发展计划）的研究经费 供引用 Sudakova, A. E., & Dahel, D. M. S. (2023). Funding the Higher Education System: International Experience and Russian Practice. R-Economy, 9(2), 207–225. doi: 10.15826/recon.2023.9.2.013 Introduction Education is a significant factor in economic growth. Many studies have proven a positive cor- relation between investment in a country’s human capital and its economic development (Hanushek, 2015). Countries that recognize the importance of knowledge, flourish, whereas those that do not - hinder their own socio-economic development. However, these conclusions are not unanimous, worth noting are studies that give other argu- ments (Benos, 2013). Nevertheless, many nation- al (Kolosnitsyna, 2021) and foreign (Maneejuk, 2021) researchers have come to the conclusion that investment in education has a positive effect http://r-economy.com mailto:ae.sudakova@gmail.com mailto:mustafaaladli@gmail.com 209 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 on developed countries. Developing and main- taining regional higher education systems allows regions to develop and prevent the outflow of the younger generation (Maneejuk, 2021). Converse- ly, increased public spending on higher education in developing countries with low levels of primary education does not have a positive effect. At the same time, the discussion about the principles for distributing higher education funding in different types of economy is still open. On the one hand, there are arguments for basing funding on competition and quasi-competition (Agasisti, 2020), leading to differentiation of universities within the system. On the other hand, it is noted that “in poorer developing countries, competition and market mechanisms will not work, and it is necessary to invest in higher education as a public good” (Marginson, 2006: 36). Musgrave R.A. drew attention to this much earlier (Musgrave, 1969): he advocated higher levels of public investment in higher education for countries with lower levels of economic development, and lower levels of public investment for countries with higher levels of economic development. Yonezawa A. and Kaiser F. also support the idea of state control, arguing that “only through actions taken at the state level, specific recommendations set out in the corresponding documents of the 1998 World Conference on Higher Education, are implemented” (Yonezawa, 2003 ). Figure 1 shows public spending on higher education in relation to GDP. Total public and private expenditure on higher education in Russia is about 1% of GDP, which is 2.5 times less than the maximum value shown in the figure. If these costs are recalculated per student, the gap is even wider: it is twice less than the average for the Eurozone countries, 2.1 times less than in Germany; 2.3 times less than in Japan; 3 times less than in Great Britain; and 3.5 times lower than in the USA (Figure 2). Within the framework of this study, we will outline the following challenges for the higher education system: the need to invest in education as a factor in the development of the country’s and the region’s economy, the dependence of higher education in many countries on state funding, and for the Russian Federation in particular, we can note the lack of financing in comparison to international practice. Figure 1. Higher education spending as a share of GDP worldwide 2019 Source: Data on Russia taken from Form № VPO-2 (2021). URL: https://minobrnauki.gov.ru/action/stat/highed/; OECD (2022). Resourcing Higher Education in Portugal. OECD Publishing, Paris. 170 р. https://doi.org/10.1787/a91a175e-en. http://r-economy.com https://minobrnauki.gov.ru/action/stat/highed/ https://doi.org/10.1787/a91a175e-en 210 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 The objective of the study is to analyze foreign and Russian experience in financing the higher education system, to identify the principles of financing in the Russian higher education system. Based on their experience in the field of higher education research, the authors hypothesize that the mechanisms for distributing funding are based on two principles: on the principle of competition and on the principle of supporting less competitive units of the system. Theoretical basis The main sources of funding for higher education are: public, private and foreign funds. However, this list can be detailed further. Thus, Chernova E. (Chernova, 2017) distinguishes 8 sources, identifying subgroups in each main group (private funds: donations, funding received from companies, self-financing, etc.). For a long time, public expenditure was the main source of funding for universities in most countries (US universities (Geiger, 2004; Becker, 1993 ), except for countries in South and East Asia and Latin America (Varghese, 2021). However, public funding still plays an important role: based on surveys, it can be noted that “in Europe, public expenditure is much more significant than in the US (Varghese, 2015: 209)”, and remains the dominant source of funding, accounting for 50 to 90 % of university income (Estermann, 2013). Thus, in Germany core funding from state grants accounts for about 80% of the universities’ institutional income (Estermann, 2022: 33); French universities receive 80% of their income as public funding (Foret, 2021), Portuguese universities this figure reaches an average of 64%, including R&D grants from public funds (OECD, 2022b: 711) (Figure 3). Higher education institutions in Russia, including private universities, receive 61% of their finances from public funds, (public universities – 63%). This distribution of funding between private and public sources reflects the position of Marginson S., Musgrave R. - in developing economies, public funding gives a positive return. Until recently, state programs of financing science and higher education brought positive effects - an increase in publication activity (Prakhov, 2021; Matveeva, 2021), a change in the researchers’ migration trajectory, an increase in their mobility (Sudakova, 2021), higler positions of universities in international rankings etc. However, the latest destructive geopolitical events allow us to 1 OECD (2022b). OECD Statistics – Education and Train- ing, OECD. Stat, https://stats.oecd.org/ Figure 2. Total expenditure on tertiary educational institutions per full-time equivalent student, 2019 Source: Data on Russia taken from Form № VPO-2 (2021). URL: https://minobrnauki.gov.ru/action/stat/highed/; OECD (2022). Total Expenditure on Educational Institutions per full-time Equivalent Student (2019): In Equivalent USD Converted Using PPPs for GDP, Direct Expenditure within Educational Institutions, by Level of Education, in Education at a Glance 2022: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/80998d78-en. http://r-economy.com https://minobrnauki.gov.ru/action/stat/highed/ https://doi.org/10.1787/80998d78-en 211 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 form only evaluative conclusions about the new challenges brought by these changes. So, despite the fact that almost all countries have the same sources of funding, in different countries they are, firstly, presented in different proportions, and secondly, there are different mechanisms for distributing this funding (Table 1). ). Table 1 Mechanisms for distributing higher education funding Mechanisms for distributing funding Characteristic Countries Block grants, including: Include three components: on a historical basis, via a funding formula or through a performance contract European countries 1) Perfor- mance-based funding Distribution of a fixed amount of money among institutions based on their rela- tive performance. Performance or development contracts, and goal-setting agree- ments whereby certain goals are agreed between the sponsor and the university, also related to performance-based funding, although they do not always have a direct impact on funding and vary in nature. 2) Funding Formula Input indicators are used (the number of students enrolled); in addition, other indicators, with less weighting, are used in the formula, and they differ between countries (doctoral degrees, international activities, etc.) 3) Performance Contracts The amount allocated for a specific purpose as a result of negotiations between universities and distributed by the university. In some countries (Germany - 2% and 5%, the Netherlands - 7%, Denmark - 1%, the Netherlands, Austria, Finland, Latvia - up to 100%), the legislation sets the minimum percentage of the total university funding. Evaluation of the achievement of results is not always strict, sometimes it serves as a tool for regulating the university management policy. 13 coun- ties-members of OECD, some states of the USA (OECD, 2019a) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% United Kingdom Australia United States Canada New Zealand Russia* Russia* st uni Korea Israel Italy Ireland Japan Estonia Portugal Slovak Republic Hungary Netherlands Greece Czech Republic Spain Denmark France Germany Belgium Sweden Poland Finland Türkiye Mexico Austria Norway Central govt. State govt. Local govt. International Other private Households Total private Figure 3. Expenditure on higher education institutions by source (Share of expenditure on public and government-dependent HEIs by source, 2018) Source: Data on Russia taken from Form № VPO-2 (2021). URL:https://minobrnauki.gov.ru/action/stat/highed/; OECD (2022): OECD Indicators – Education at a Glance 2022. 462 p. https://doi.org/10.1787/3197152b-en http://r-economy.com https://minobrnauki.gov.ru/action/stat/highed/ https://doi.org/10.1787/3197152b-en 212 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Mechanisms for distributing funding Characteristic Countries Excellence Pro- grams Funding is channeled into research and supporting large initiatives. The amount of allocated funds and directions are much wider than with research grants (research initiatives, campus construction, establishing doctoral schools, regional integration of universities, etc.). The standard is the programs of Germany and France. Similar programs exist in China, Russia, and other countries. Itemized distribu- tion of funding Funds are allocated strictly according to the items of expenditure, there is a tight restriction on the redistribution of funds between budget items. Such a mechanism results in a high level of financial control and transparency for the central budgetary authorities, but drastically limits the ability of institutions to take responsibility for resource management and make strategic decisions on resource prioritization. Rarely in Euro- pean countries; remain the main mechanism in Greece (OECD, 2018), Korea, state universi- ties in Mexico (OECD, 2019), public universi- ties of the Russian Federation (Agasisti, 2020) Competition-based (grants, projects) Funds are allocated on a competitive basis, as a rule, from research founda- tions, for a limited period, aimed at supporting research, human resources, and infrastructure. The majority of countries, includ- ing Russia Source: Budget Code of the Russian Federation No. 145-FZ of July 31, 1998 (as amended on December 28, 2022), article 161; OECD (2018). Education for a Bright Future in Greece, Reviews of National Policies for Education, OECD Publishing, Par- is. https://dx.doi.org/10.1787/9789264298750-en; OECD (2019). The Future of Mexican Higher Education: Promoting Quality and Equity, Reviews of National Policies for Education, OECD Publishing, Paris. https://dx.doi.org/10.1787/9789264309371-en; OECD (2019a). University-Industry Collaboration: New Evidence and Policy Options, OECD Publishing, Paris, https://dx.doi. org/10.1787/e9c1e648-en. Figure 4 shows the relationship between mechanisms and classification characteristics. This study distinguishes three principles for financing higher education: • competition/quasi-competition-based: the more competitive units of the system are financed. However, the mechanisms for distributing finances based on competition lead to high differentiation within the system and compression of the number of units of the system; • based on equalization: all units of the system are financed, including the less competitive ones; the principle can be carried out through allocating places at universities paid for by state subsidies, maintaining competitive wages, developing infrastructure in order to preserve and evenly distribute human capital across regions; • based on investment: funding can be used to develop the infrastructure of higher education, to enhance universities’ competitiveness, and to develop promising areas of training. In European countries, funding is a block grant (Pruvot, 2015; OECD, 2020) 2 (table 1) which 2 OECD (2020). Resourcing Higher Education:  Chal- lenges, Choices and Consequences. Higher Education. OECD Publishing, Paris, https://doi.org/10.1787/735e1f44-en. includes three components; the ratio between these three components is different in different universities, and funding in some European countries is long-term, i.e. the funding budget is set for up to four years (Pruvot, 2017). The considered mechanisms correlate with the mechanisms of financing higher education in the Russian Federation. Performance-based funding and funding formula are represented in the Russian Federation by subsidies provided to universities as part of the implementation of the government assignments3. The founders determine the volume of public services for organizations when devising the state assignment for the provision of public services for the next financial year and planning period, and when assessing the achievement of indicators of the volume of public services in the reporting financial year. Permissible deviations from the target indicators of the state 3 Order of the Ministry of Science and Higher Ed- ucation of the Russian Federation of October 28, 2021 No. 989 “On approval of the methodology for the for- mation of the government assignment for the provision of educational services in the field of higher and second- ary vocational education for the next financial year and planning period” http://r-economy.com https://dx.doi.org/10.1787/9789264298750-en https://dx.doi.org/10.1787/9789264309371-en https://dx.doi.org/10.1787/e9c1e648-en https://dx.doi.org/10.1787/e9c1e648-en https://doi.org/10.1787/735e1f44-en 213 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 assignment are set5. Funding formula manifests itself in the cost of providing one unit of service and achieving absolute values. For example, the target admission figures determine the number of places (hereinafter referred to as TAF) for which the university can attract students with the appropriate exam scores; later on, the university receives state funding based on the number of enrolled students, but not more than the TAF. Excellence programs are implemented not only in European countries, but are also a tool for supporting universities’ development in China (the “211” and “985” projects, etc.), and Russia (5/100 Program, Priority 2030, campus construction programs). In the Russian Federation, programs are implemented based on competition, on achieving roadmap indicators, i.e. set target indicators with certain funding: funding for the next financial year is distributed based on the achievement of the current year indicators. With regard to the distribution of funding through research foundations, the distribution mechanism is also based on competition, with the focus on achieving the set targets. 4 The figure was updated on August 16, 2023 (the content remained unchanged). 5 Order of the Ministry of Education and Science of the Russian Federation of March 15, 2021 No. 172 “On approval of the procedure for determining and applying the permissible (possible) deviations of the values of quality indicators and (or) volume from the established values of quality indicators and (or) the volume of public services when devising government assignments for the provision of public services for a federal state budgetary or autonomous for which the Ministry of Sci- ence and Higher Education of the Russian Federation exercises the functions and powers of the founder” Higher education in the Russian Federation is funded through the students’ (their households’) finances as follows: • targeted funding - a small share in the structure of Russian universities’ income (4% of all funds for educational activities in general for universities of the Russian Federation6): tuition is paid by an organization in which a university graduate must work for a predetermined period (3 years, as a rule) after graduation. Financing is provided by agreement between households and the organization, to a specific university or a university of their choice, the choice of the degree field is carried out in the same way; • household funds (private funding) are the main source of funding in some areas of training; • public funds through the provision of the number of “state-funded” places which are determined by the admission targets and represent a complex mechanism7. This mechanism can 6 VPO-2 form “Information on the material, technical and information base, financial and economic activities of an educational organization of higher education” // Ministry of Education and Science of Russia. 2022. 7 Order of the Ministry of Science and Higher Education of the Russian Federation of November 1, 2021 No. 996 “On approval of the Procedure for conducting the competition for the distribution of target admission figures by areas of training and (or) enlarged groups of specialties and areas of training for studying in educational programs of higher education, and also by groups of research specialties and (or) research specialties for training programs for the training of research and pedagog- ical personnel in post-graduate school in state-funded places financed from the federal budget” // Official Internet portal of legal information. URL: http://publication.pravo.gov.ru/ Doc- ument/View/0001202111250038 (accessed on 05.02.2023) Figure 4. Correlation between mechanisms for financing higher education and principles of distribution Source: compiled by the authors4 http://r-economy.com http://publication.pravo.gov.ru/ Document/View/0001202111250038 http://publication.pravo.gov.ru/ Document/View/0001202111250038 214 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 combine all the three principles (Figure 4). We assume that the equalization principle manifests itself when state-funded places are provided to remote regions with less competitive units of the system in order to preserve human capital in the regions, and investment principle is used for developing promising areas of training, or those that are important for the socio-economic sphere, but are less popular with applicants. Thus, different mechanisms for financing higher education form different systems and their characteristics: increasing the gap between universities (Agarkov, 2019; Abankina, 2013), monopolizing the units of the system (Sudakova, Sandler, 2022), creating competitive universities and world-class universities, ensuring equal opportunities for higher education (allocating quotas for applicants from low-income families (Dill, 1997), developing infrastructure. Building a full picture of the state of higher education will make it possible to respond to the socio-economic, technological and global challenges of our time in a timely manner. Despite the fact that there are a large number of studies focused on diagnosing the state of higher education (Sandler, 2020; Sandler, 2021), on assessing the degree of differentiation of the units within the system (Kuzminov, 2014), in our opinion, they overlook an important characteristic of the higher education system - principles for the distribution of funding. Methods and data The principles of distribution of resources, in particular public funding in the Russian higher education system, are identified through economic and statistical analysis methods. The statistical base for the study is the data of a large- scale higher education monitoring project of 2019-2021 (Education in Russia, 2021)8,9. The database has statistical information for the period of 2019-2021, consisting of indicators for more than 650 public and private Russian univer- sities (2019 - 681, 2020 - 670, 2021 - 662 universi- ties). The indicators used for the study include the number of students, the university’s income, the number of faculty members, and publication ac- tivity. All indicators are detailed by funding sourc- es, mode of education, etc. The study tests the hypothesis about the distribution of funding shown in Figure 4, assuming that universities located in regions with less competitive socio-economic characteristics, are funded according to the principle of equalization, i.e. universities in these regions are financed with less reference to qualitative indicators (for example, a decrease in the average USE score; number of students in state-funded places; funding is equal to that of more competitive universities), in order to enhance their competitiveness. Universities were divided into groups and subgroups for the analysis. To assess the socio-economic state of the region, the following indicators for express analysis were selected: the share of gratuitous revenues 8 Information and analytical materials based on the results of monitoring the activities of educational institutions of high- er education // MIREA - Russian Technological University. The main information and computing center. URL: www.miccedu.ru 9 Education in Russia - 2021. Statistical Bulletin. Moscow: MIREA Russian Technological University, 2021. 363 p. Figure 5. The share of public funds for educational activities in the total income of public and private universities Source: compiled by the authors based on the «Data on Russia taken from Form № VPO-2 (2021). URL: https://minobrnauki.gov.ru/action/stat/highed/» http://r-economy.com URL: www.miccedu.ru https://minobrnauki.gov.ru/action/stat/highed/ 215 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 in the structure of budget income - shows how independent the budget is; GRP per capita – shows how rich the region is; migration growth - we assume that the negative value of this indicator evidences the unattractiveness of the region. In order to identify the equalization mechanism, it seems relevant to analyze revenues from budgets of all levels, since income from educational activities accounts for 66% of the total income of universities (2021), while the volume of finances for educational activities from the budgets of all levels accounts for 43% of the total income of universities. Figure 5 shows the grouping of universities according to the share of public funds for educational activities in their total income, the intersection of the X and Y values shows how many universities have a similar share. For example, in 2019, one university was 100% funded from the state budget, and all funding was directed to educational activities; the share of public funding for educational activities of 35 universities was 0. The graph for the share of federal funds in the structure of university income looks similar. Since the study reveals that university financing is based on equalization, of interest are only those universities that have a share of federal funding in their budget structure. One of the first objectives of the study is to distinguish groups of regions according to their socio-economic characteristics: GRP per capita; migratory inflow; income structure of the consolidated budget; priority regions for development10. The regions of the Russian Federation are highly differentiated, and the support of some regions is already reflected in the level of distribution of inter-budgetary transfers (Sudakova, Agarkov, 2022). Thus, the indicators allow us to rank the objects of study, and categorise them into several groups. Using the SPSS software application, the subjects of the Russian Federation are divided into 4 clusters - Figure 6 shows data visualization in three-dimensional space. Table 2 presents the quantitative description in each group. 10 If we focus on the state program “Balanced Regional Development”, then it covers a relatively small number of the constituent entities/subjects of the Russian Federation: the Ka- liningrad region, the North Caucasian region, the Far Eastern Federal District, the Republic of Crimea and the city of Sevas- topol. Universities located in these subjects of the Russian Fed- eration will be analyzed separately. Table 2 Characteristics of subjects (regions) by groups Group num- ber Migration inflow, thousands of people Average value Minimum value Maximum value Number of subjects Negative value Positive value 1 10,7 - 2,9 23,9 2 4 2 9,4 - 4,3 113,0 10 23 3 1,0 - 7,6 15,4 19 21 4 2 -1,5 9,3 2 2 Group number GRP per capita, thousand rubles/person Average value Minimum value Maximum value Number of subjects * Below the average value in the RF Above the average value in the RF 1 1 112 9542 1 568 0 5 2 555 454 757 3 30 3 325 142 425 40 0 4 2 790 1 994 5 072 0 4 Group number Share of gratuitous receipts (subsidies, donations), % Average value Minimum value Maximum value Number of subjects** under 10% 11-39% over 40% 1 18,9 2,7 52 2 3 1 2 23 5,8 48 2 29 1 3 47 17 87 0 16 24 4 13 4,5 35 3 1 0 Note: * to indicate the boundaries of the indicator, the average value for the Russian Federation is used, however, the indicators of the super-rich subjects of the Russian Federation (Moscow, YNAO, KhMAO, Magadan, Sakhalin regions) are excluded; average value - 467 thousand rubles (2021); ** the boundaries of the level of gratuitous receipts are presented in accordance with paragraph 3 and paragraph 4 of Article 130 of the Budget Code, the designated boundaries provide for a different level of authority of the subjects. Source: compiled by the authors based on the gks.ru http://r-economy.com http://gks.ru 216 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Table 3 shows the regions included in the group, and the characteristics of each group. Thus, in terms of the identified socio-economic characteristics, the regions of groups 4 and 1 are the most favorable, and the subjects of group 3 have the worst characteristics. Table 3 Characteristics of groups of regions Group Group description Number of subjects in the group, representatives 1 Most subjects are characterized by a positive migration balance (for 4 out of 6), GRP per capita is several times higher than the average level in the Russian Federation, while the share of gratuitous receipts has a different value in the group, but mostly no more than 39%. 6 subjects Moscow, Krasnoyarsk Territory, St. Petersburg, Murmansk Region, Republic of Sakha, Kamchatka Territory 2 Most of the subjects in the group have a positive migration balance (23 out of 33), the average GRP per capita is slightly higher than the average for the Russian Federation, the share of gratuitous receipts is in the range of 11-39%. 33 subjects, Belgorod Region, Republic of Tatarstan, Perm Territory 3 GRP per capita is the lowest in the country, and the value is below the average for the Russian Federation; 50% of the subjects in the group have a negative balance; for 50% of the subjects the share of gratuitous receipts is mainly in the range of 11-39%, for the rest it is more than 39%. 40 subjects, Pskov region, Republic of Kalmykia, Dagestan, Chechnya 4 Positive balance of migration growth (with the exception of Yamal- Nenets Autonomous Okrug and the Magadan region), a low share of gratuitous receipts, GRP per person is several times higher than the average - the highest values in the Russian Federation. 4 subjects, Khanty-Mansi Autonomous Okrug, Yamal-Nenets Autonomous Okrug *, Sakhalin Region, Magadan Region Note. * Yamalo-Nenets Autonomous Okrug is included in group 4; however, the region is excluded from further analysis, as it does not include universities that have students studying in state-funded places. Source: compiled by the authors Figure 7 shows a cartographic visualization of the distribution of regions (subjects) by groups. Figure 6. Visualization of groups in 3D space Source: compiled by the authors based on the gks.ru in SPSS http://r-economy.com 217 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Figure 7. Groups of Russian Federation regions according to socio-economic characteristics Source: compiled by the authors Further analysis is based on the values of university performance indicators; the average values in subgroups are determined, the correlation between indicators is calculated, and a data reliability test is carried out. Results First, 4 groups of regions were identified based on their socio-economic characteristics. Further analysis is carried out within these groups, with universities divided into subgroups: (1) all universities within the group, (2) universities located in regions with positive migration (PM), (3) universities located in regions with negative migration (NM), (4) universities located in regions with negative migration and a share of gratuitous receipts of more than 40% (NM GR >40). The analysis for (2), (3), (4) is presented in the context of universities with a “special” status (hereinafter “_s”), i.e. those that are program participants (5/100, priority-2030) and/or those that have been assigned a status (National Research University - NRU, Federal University - FU, etc.) and universities without a status and/or not participating in programs (hereinafter “_w”). In addition, data are presented separately for all universities, without dividing them into groups of regions. Universities in group 1 are represented by heterogeneous characteristics: thus, along with more attractive regions and universities in Moscow and St. Petersburg, the group includes universities in the Murmansk region, the Republic of Sakha, and the Kamchatka Territory. The calculation of average values showed (Table 4) that as the region’s socio-economic indicators deteriorate, the share of income from the federal budget increases, mainly due to students studying in state-funded places, and so does the share of such students, while the average USE score of students in state-funded places lowers (Figure 8a). The situation with the indicators of research activity is different: they increase as the region’s socio-economic indicators improve, and universities with and without special status show the same trend, but universities that participate in state programs and/or those with a special status have higher indicator values (Figure 8b). Despite these two divergent trends, it should be noted that the university’s total income per academic staff member increases as the socio- economic characteristics become worse, and participating universities and/or universities with status have higher values (Figure 8b). http://r-economy.com 218 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Ta bl e 4 A ve ra ge v al ue s fo r th e in di ca to rs o f u ni ve rs it ie s by g ro up s of r eg io ns G ro up 1 2 3 4 A ll un iv er si ti es Su b- gr ou ps A ll un i- ve rs iti es in th e gr ou p 1_ s 1_ w A ll un i- ve rs iti es in th e gr ou p 2_ s PM 2_ w P M 2_ s N M 2_ w N M A ll un i- ve rs iti es in th e gr ou p 3_ s PM 3_ w P M 3_ s N M 3_ w N M 3_ s N M G R >4 0 A ll un i- ve rs iti es in th e gr ou p PM N M al l_ s/ _w P M al l_ s/ _w N M N um be r of re gi on s 6 4 6 33 18 20 7 9 40 12 20 11 18 5 11 4 50 33 35 6 18 46 N um be r of u ni ve rs iti es 15 3 46 10 7 21 1 38 12 9 9 35 15 4 18 59 12 65 5 33 6 39 8 12 6 10 3 11 4 21 29 5 A ve ra ge n um be r of un iv er si tie s in th e re gi on , un iv er si ty /o ne re gi on 25 12 17 ,8 6 2 6, 5 1 3, 9 4 2 3 1 3, 6 1 3 2 8 4 3 19 1 6, 4 Sh ar e of s tu de nt s st ud yi ng in s ta te -f un de d pl ac es , % 60 64 56 53 48 50 57 59 57 54 60 52 58 55 60 61 55 58 52 50 54 55 A ve ra ge U SE s co re fo r st ud en ts s tu dy in g in s ta te - fu nd ed p la ce s, u ni ts 64 71 62 65 71 63 69 64 62 66 59 66 62 57 56 40 62 63 69 51 67 60 A ve ra ge U SE s co re fo r st ud en ts p ay in g fo r th ei r tu iti on th em se lv es 55 60 54 57 62 56 60 56 56 59 55 56 57 57 56 35 55 56 61 47 59 54 Sh ar e of in co m e fr om th e fe de ra l b ud ge t, % 67 67 64 60 58 56 62 67 65 61 62 61 67 69 69 54 59 69 59 46 63 58 Sh ar e of in co m e fr om ed uc at io na l a ct iv iti es , % 72 78 69 67 63 63 69 74 70 63 68 69 73 68 76 74 66 74 66 61 69 65 In co m e fo r ac ad em ic s ta ff, ru bl es /p er so n 46 50 51 50 44 87 33 09 34 57 29 80 39 11 34 94 26 76 27 24 25 07 28 22 28 25 28 76 28 59 54 95 31 58 32 06 34 11 49 17 32 46 30 36 In co m e fr om R & D fo r ac ad em ic s ta ff, r ub le s/ pe rs on 30 9 68 1 23 5 23 8 45 2 16 7 34 3 13 0 16 4 24 1 14 4 24 0 15 0 20 0 13 4 21 2 23 6 15 8 39 8 25 7 28 0 17 1 http://r-economy.com 219 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 G ro up 1 2 3 4 A ll un iv er si ti es Su b- gr ou ps A ll un i- ve rs iti es in th e gr ou p 1_ s 1_ w A ll un i- ve rs iti es in th e gr ou p 2_ s PM 2_ w P M 2_ s N M 2_ w N M A ll un i- ve rs iti es in th e gr ou p 3_ s PM 3_ w P M 3_ s N M 3_ w N M 3_ s N M G R >4 0 A ll un i- ve rs iti es in th e gr ou p PM N M al l_ s/ _w P M al l_ s/ _w N M N um be r of c ita tio ns in W oS p er 1 00 a ca de m ic s ta ff m em be rs , u ni t/ pe rs on 19 2 45 8 13 3 15 5 34 5 16 3 17 5 87 86 14 3 90 12 5 70 14 6 56 36 14 1 86 28 0 17 9 14 4 12 4 N um be r of c ita tio ns in Sc op us p er 1 00 a ca de m ic st aff m em be rs , u ni t/ pe rs on 25 3 62 2 16 8 20 2 43 3 19 2 27 7 11 6 12 7 22 0 11 9 20 0 11 1 21 3 96 74 18 3 13 3 37 2 21 6 23 0 15 3 N um be r of fu ll- tim e st ud en ts fo r ac ad em ic s ta ff, st ud en t/ on e st aff m em be r 10 11 10 11 12 11 12 11 11 12 10 13 10 13 10 11 11 11 12 11 13 10 N um be r of s tu de nt s fo r ac ad em ic s ta ff, s tu de nt /o ne st aff m em be r 16 14 17 19 19 20 18 19 19 17 18 22 19 24 20 24 18 20 17 20 21 19 Sh ar e of fu ll- tim e st ud en ts , % 67 78 59 62 66 56 68 61 59 70 56 59 57 58 57 48 62 58 69 53 62 56 Sh ar e of s tu de nt s in ba ch el or ’s de gr ee p ro gr am s, % 68 63 69 66 62 69 68 65 69 59 72 66 65 67 67 83 69 67 61 75 67 71 N ot e: “ 1_ s” (2 _s ; 3 _s ) - ca lc ul at io n fo r u ni ve rs iti es w ith a sp ec ia l s ta tu s, i.e . p ar tic ip at in g in th e pr og ra m s ( N U /F U , L ea di ng E ng in ee ri ng S ch oo ls - LE S, 5 /1 00 , P ri or ity 2 03 0) a nd “ 1_ w ” (2 _w ; 3 _w ; 4 _w ) – u ni ve rs iti es w ith ou t a sp ec ia l s ta tu s. N M a nd P M - ca lc ul at io n fo r un iv er si tie s l oc at ed in re gi on s w ith n eg at iv e an d po si tiv e va lu es o f m ig ra tio n flo w s, N M G R > 40 - ca l- cu la tio n fo r u ni ve rs iti es lo ca te d in re gi on s w ith n eg at iv e va lu es o f m ig ra tio n flo w s a nd w ith a sh ar e of g ra tu ito us re ce ip ts o f m or e th an 4 0% (c al cu la tio n ac co rd in g to p ar ag ra ph 4 , a rt ic le 1 30 of th e R F Bu dg et C od e) . 1 , 2 , 3 , 4 - un iv er si tie s l oc at ed in re gi on s o f g ro up s 1 , 2 , 3 , 4 . So ur ce : c om pi le d by th e au th or s b as ed o n th e «D at a on R us si a ta ke n fr om F or m № V P O -2 (2 02 1) . U R L: h tt ps :// m in ob rn au ki .g ov .r u/ ac tio n/ st at /h ig he d/ » http://r-economy.com https://minobrnauki.gov.ru/action/stat/highed/ 220 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Ta bl e 5 C oe ffi ci en t o f v ar ia ti on b y gr ou ps , % G ro up 1 2 3 A ll un iv er si ti es Su b- gr ou ps A ll un i- ve rs it ie s in th e gr ou p 1_ s A ll un i- ve rs it ie s in th e gr ou p 2_ s P M 2_ s N M A ll un i- ve rs it ie s in th e gr ou p 3_ s P M 3_ s N M 3_ s N M G R > 40 P M N M al l_ s P M al l_ s N M al l_ w G R > 40 Sh ar e of s tu de nt s st ud yi ng in s ta te -f un de d pl ac es 9 7 11 13 9 10 11 14 14 11 8 13 12 15 A ve ra ge U SE s co re fo r st ud en ts s tu dy in g in s ta te - fu nd ed p la ce s 5 10 5 5 3 5 4 4 5 11 4 5 4 5 A ve ra ge U SE s co re fo r st ud en ts p ay in g fo r th ei r tu iti on th em se lv es 8 7 7 4 4 7 3 3 2 11 6 4 4 2 Sh ar e of in co m e fr om th e fe de ra l b ud ge t 13 11 11 7 8 11 11 13 12 13 8 10 11 17 Sh ar e of in co m e fr om ed uc at io na l a ct iv iti es 11 7 19 13 16 10 10 12 9 20 17 19 22 33 In co m e fo r ac ad em ic s ta ff 10 16 14 16 26 11 10 11 13 20 17 19 22 13 In co m e fr om R & D fo r ac ad em ic s ta ff 49 46 44 40 26 32 41 32 37 43 31 47 32 37 N um be r of c ita tio ns in W oS p er 1 00 a ca de m ic s ta ff m em be rs 42 35 65 78 38 64 46 72 91 67 51 79 56 91 N um be r of c ita tio ns in Sc op us p er 1 00 a ca de m ic st aff m em be rs 40 37 59 71 38 46 47 64 91 59 42 70 52 91 N um be r of fu ll- tim e st ud en ts p er a ca de m ic s ta ff m em be r 6 8 7 8 7 9 11 11 13 8 8 9 10 13 N um be r of s tu de nt s pe r ac ad em ic s ta ff m em be r 14 6 10 14 11 13 12 11 12 11 11 13 13 12 Sh ar e of fu ll- tim e st ud en ts 7 3 7 14 11 8 10 10 14 8 8 11 11 14 Sh ar e of b ac he lo r’s d eg re e st ud en ts 8 8 7 14 11 9 23 10 11 8 10 17 10 11 So ur ce : c om pi le d by th e au th or s b as ed o n th e «D at a on R us si a ta ke n fr om F or m № V P O -2 (2 02 1) . U R L: h tt ps :// m in ob rn au ki .g ov .r u/ ac tio n/ st at /h ig he d/ » http://r-economy.com https://minobrnauki.gov.ru/action/stat/highed/ 221 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 8a) 8b) Figure 8. Comparison of average values in university sub-groups Source: compiled by the authors based on the «Data on Russia taken from Form № VPO-2 (2021). URL: https://minobrnauki.gov.ru/action/stat/highed/» Table 5 shows the coefficient of variation for groups and sub-groups, the deviation of the data from the average value in the sample is within 10. However, if the average USE score in groups 2 and 3 is within 5, then the variation of specific indicators (“Income from R&D per academic staff member” and citation rates) exceed the allowable values (more than 30%), which indicates the heterogeneity of the sample according to these indicators. In order to clarify the mechanisms for distributing funding, we conducted a correlation analysis of research-related indicators (Table 6): a positive correlation between the indicators will allow us to conclude that funding is distributed based on competition, while the absence of such correlation means distributions based on equalization. Table 6 Indicators of correlation coefficients by indicators of universities Coefficients 2_s PM 2_s NM 3_s, PM, GR < 40 _s PM _s, PM, GR < 40 _s NM University income from R&D and the number of citations in WOS** 0,4 0,3 0,6 0,5 0,4 0,3 University income from R&D and the number of citations in Scopus** 0,4 0,4 0,7 0,5 0,5 0,4 University income from R&D and the number of publications in WOS*** 0,6 0,5 0,7 0,6 0,6 0,4 University income from R&D and the number of publications in Scopus*** 0,6 0,7 0,6 0,6 0,5 0,5 Data validity (calculated in SPSS, 95% confidence interval) 100% 100% 100% 100% 100% 100% Cronbach’s alpha reliability coefficient based on standardized items α>0,7 α>0,8 α>0,75 α>0,7 α>0,7 α>0,6 Cronbach’s alpha reliability coefficient based on standardized items α>0,6 α>0,5 - α>0,6 α>0,6 α>0,5 Note: *The number of students admitted for full-time studies in bachelor’s and specialist’s degree programs in state-funded places based on the results of the Unified State Examination; **The number of citations of papers published over the past 5 years, indexed in the information and analytical system Web of Science Core Collection (or Scopus), per 100 faculty members; ***The number of publications indexed in the Web of Science Core Collection (or Scopus) per 100 faculty members. Source: compiled by the authors based on the «Data on Russia taken from Form № VPO-2 (2021). URL: https://minobrnauki. gov.ru/action/stat/highed/» http://r-economy.com https://minobrnauki.gov.ru/action/stat/highed/ https://minobrnauki.gov.ru/action/stat/highed/ https://minobrnauki.gov.ru/action/stat/highed/ 222 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 There is a correlation between the following indicators: (*) income from R&D and (*) the number of publications (Table 6), with the value of the correlation coefficient decreasing as the region’s socio-economic characteristics deteriorate. It should be noted that the correlation coefficient between similar indicators for universities without a status is below 0.4. The presented data allow us to make a conclusion about a possible difference in the mechanism for distributing funding among different groups of regions. Thus, regions with less favorable socio- economic conditions receive budget funding with a lesser focus on university performance indicators. Another pattern was identified: as the socio- economic characteristics of the region improve (in our case, universities in group 3), funding is directed to the development of research activity based on the principles of equalization. The correlation between the indicators of the universities’ educational activities allows us to draw a conclusion regarding the mechanism for distributing funding: less correlation between indicators related to research activities is observed among universities in group 3, as well as among universities without a special status in group 2. Our study comes to a slightly different conclusion than that presented in the work of Abankina I.V., which states that “foreign countries try to increase financial support for higher education and the system’s flexibility, and not pursue a policy of focusing efforts solely on supporting leaders, like in Russia” (Abankina, 2019). Conclusion The study is aimed at determining the principles for the distribution of funding among Russian universities. The significance of the study is justified by the fact that the importance of financing higher education as a means of investing in the public good has been proved more than once. Thus, the study analyzed foreign funding mechanisms and compared them with Russian practice, which has similar foundations: competition-based funding (mainly grants from research foundations), development programs, funding by formula (government assignments), etc. The analyzed funding mechanisms are classified according to the principle of distribution. Based on the accumulated experience, the study puts forward a hypothesis about the possible existence of two principles for financing Russian higher education. The hypothesis was partially confirmed. However, the additional findings obtained, expand the understanding of higher education funding. First, as the region’s socio-economic characteristics deteriorate, there is an increase in the value of some indicators (for example, an increase in the share of income from the federal budget; the share of students studying in state- funded places, the number of students per 1 teaching staff member) and a significant decrease in other indicators (for example, the average USE score, the share of full-time students). These data do not allow us to make an unambiguous conclusion that the hypothesis was confirmed, since the change in indicators may be due to lower competition among university applicants (as a result, there is a decrease in the average USE score, and an outflow of applicants with higher scores to other regions); the increase in the share of federal funding and the share of students studying in state-funded places may be due to lower demand for non-state-funded (“commercial”) places. However, an important addition to these conclusions is the increase in the indicator of income per academic staff member as the socio-economic characteristics of the region where the universities (within the group) are located, deteriorate. Secondly, one more conclusion related to scientific activity was made during the study. As the region’s socio-economic characteristics improve, the values of indicators related to research activity also improve. The analysis carried out allows us to make the following conclusions: the distribution of funding among Russian universities is based on the principles of quasi-competition (analysis of data on universities with a special status) and equalization (as the region’s socio-economic characteristics deteriorate). Universities located in regions with low indicators of socio-economic development are financed mainly based on the principle of equalization, for the most part, with respect to educational activities, and, as the socio-economic situation in the region improves, funding based on equalization affects the universities’ research activities. Universities in group 4 stand out from the general trend (the so- called “wealthy northern regions”). Another more obvious conclusion is that research activities of universities that participate in state programs are funded on competitive basis, http://r-economy.com 223 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 while other universities have less correlation between the indicators, as a result of which we can assume that research activities of other universities are funded based on the principles of equalization. And a less obvious conclusion is that the correlation between the indicators of educational activities in universities-participants of state programs is low and in some cases negative (with high USE scores, the share of students in state-funded places is different). The results of the study expand the understanding of the principles of funding distribution in the Russian higher education system: in contrast to most studies on the concentration of resources in a limited number of universities, the study concludes that resources are also distributed based on equalization, supporting less competitive units of the system, and also that funding is sent to regions with unstable socio-economic characteristics. The study can be expanded by detailing the indicators for ranking regions, as well as adding an analysis of the areas of training in the regions to identify the principle of financing based on investment. References Abankina, I., Aleskerov, F., Belousova, V., Gokhberg, L., Zinkovsky, K., Kiselgof, S.&Shvy- dun, S. (2013) Tipologiya i analiz nauchno-obrazovatel’noy rezul’tativnosti rossiyskikh vuzov [A Typology and Analysis of Russian Universities’ Performance in Education and Research]. Foresight- Russia, 7(3), 48-63 Abankina, I.V. (2019). 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DOI: 10.1007/978-981-19-7391-8_1 Yonezawa, A. & Kaiser, F. (2003). System-Level and Strategic Indicators for Monitoring Higher Education in the Twenty-First Century, Studies on Higher Education Series, UNESCO, Bucharest. Information about the authors Anastasia E. Sudakova – Candidate of Economics, Associate Professor, Senior Researcher, Ural Federal University (19 Mira street, 620002, Russia, Yekaterinburg,); e-mail: ae.sudakova@gmail.com, https://orcid.org/0000-0002-3791-1129 Dahel Mustafa Saleh Dahel - lecturer, Al-Furat Al-Awsat Technical University (54003, Iraq, Najaf, Babylon-Najaf street, 1); e-mail: mustafaaladli@gmail.com, ORCID 0000-0002-3610-5088 ARTICLE INFO: received February 13, 2023; accepted June 16, 2023 http://r-economy.com http://dx.doi.org/10.1007/978-3-319-20877-0_11 https://orcid.org/0000-0002-3791-1129 225 r-economy.com R-ECONOMY, 2023, 9(2), 207–225 doi 10.15826/recon.2023.9.2.013 Online ISSN 2412-0731 Информация об авторах Судакова Анастасия Евгеньевна – к.э.н., доцент, старший научный сотрудник, Уральский федеральный университет (620002, Россия, г. Екатеринбург, ул. Мира, 19); e-mail: ae.sudako- va@gmail.com, ORCID: 0000-0002-3791-1129 Дахел Мустафа Салех Дахел – лектор, Аль-Фурат Аль-Авсат технический университет (54003, Ирак, г. Наджаф, ул. Вавилон-Наджаф, 1); e-mail: mustafaaladli@gmail.com, ORCID: 0000-0002-3610-5088 ИНФОРМАЦИЯ О СТАТЬЕ: дата поступления 13 февраля 2023 г.; дата принятия к печати 16 июня 2023 作者信息 苏达科娃·阿纳斯塔西娅·叶夫根涅夫娜——经济学博士，高级研究员，乌拉尔联邦大 学（邮编：620002，俄罗斯，叶卡捷琳堡市，米拉大街19号）；邮箱：ae.sudakova@ gmail.com, ORCID: 0000-0002-3791-1129 达赫尔·穆斯塔法·萨利赫·达赫尔——讲师，Al-Furat Al-Awsat技术大学（邮 编：54003，伊拉克，纳杰夫市，巴比伦纳杰夫大街1号）；邮箱：mustafaaladli@ gmail.com, ORCID: 0000-0002-3610-5088 http://r-economy.com mailto:ae.sudakova@gmail.com mailto:ae.sudakova@gmail.com mailto:ae.sudakova@gmail.com mailto:ae.sudakova@gmail.com